19,294 research outputs found
Scalable wavelet-based coding of irregular meshes with interactive region-of-interest support
This paper proposes a novel functionality in wavelet-based irregular mesh coding, which is interactive region-of-interest (ROI) support. The proposed approach enables the user to define the arbitrary ROIs at the decoder side and to prioritize and decode these regions at arbitrarily high-granularity levels. In this context, a novel adaptive wavelet transform for irregular meshes is proposed, which enables: 1) varying the resolution across the surface at arbitrarily fine-granularity levels and 2) dynamic tiling, which adapts the tile sizes to the local sampling densities at each resolution level. The proposed tiling approach enables a rate-distortion-optimal distribution of rate across spatial regions. When limiting the highest resolution ROI to the visible regions, the fine granularity of the proposed adaptive wavelet transform reduces the required amount of graphics memory by up to 50%. Furthermore, the required graphics memory for an arbitrary small ROI becomes negligible compared to rendering without ROI support, independent of any tiling decisions. Random access is provided by a novel dynamic tiling approach, which proves to be particularly beneficial for large models of over 10(6) similar to 10(7) vertices. The experiments show that the dynamic tiling introduces a limited lossless rate penalty compared to an equivalent codec without ROI support. Additionally, rate savings up to 85% are observed while decoding ROIs of tens of thousands of vertices
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant
of deep neural networks for irregular structured and geometric input, e.g.,
graphs or meshes. Our main contribution is a novel convolution operator based
on B-splines, that makes the computation time independent from the kernel size
due to the local support property of the B-spline basis functions. As a result,
we obtain a generalization of the traditional CNN convolution operator by using
continuous kernel functions parametrized by a fixed number of trainable
weights. In contrast to related approaches that filter in the spectral domain,
the proposed method aggregates features purely in the spatial domain. In
addition, SplineCNN allows entire end-to-end training of deep architectures,
using only the geometric structure as input, instead of handcrafted feature
descriptors. For validation, we apply our method on tasks from the fields of
image graph classification, shape correspondence and graph node classification,
and show that it outperforms or pars state-of-the-art approaches while being
significantly faster and having favorable properties like domain-independence.Comment: Presented at CVPR 201
A minimalistic approach for fast computation of geodesic distances on triangular meshes
The computation of geodesic distances is an important research topic in
Geometry Processing and 3D Shape Analysis as it is a basic component of many
methods used in these areas. In this work, we present a minimalistic parallel
algorithm based on front propagation to compute approximate geodesic distances
on meshes. Our method is practical and simple to implement and does not require
any heavy pre-processing. The convergence of our algorithm depends on the
number of discrete level sets around the source points from which distance
information propagates. To appropriately implement our method on GPUs taking
into account memory coalescence problems, we take advantage of a graph
representation based on a breadth-first search traversal that works
harmoniously with our parallel front propagation approach. We report
experiments that show how our method scales with the size of the problem. We
compare the mean error and processing time obtained by our method with such
measures computed using other methods. Our method produces results in
competitive times with almost the same accuracy, especially for large meshes.
We also demonstrate its use for solving two classical geometry processing
problems: the regular sampling problem and the Voronoi tessellation on meshes.Comment: Preprint submitted to Computers & Graphic
- …